Performance Case study of Grigoryan FFT over Cooley-Tukey FFT using TMS DSP Processors

Frequency analysis plays vital role in the applications like cryptanalysis, steganalysis [6], system identification, controller tuning, speech recognition, noise filters, etc. Discrete Fourier Transform (DFT) is a principal mathematical method for the frequency analysis. The way of splitting the DFT gives out various fast algorithms. In this paper, we present the implementation of two fast algorithms for the DFT for evaluating their performance. One of them is the popular radix-2 Cooley-Tukey fast Fourier transform algorithm (FFT) [1] and the other one is the Grigoryan FFT based on the splitting by the paired transform [2]. We evaluate the performance of these algorithms by implementing them on the TMS320C6748 and TMS320C5416 DSPs. We developed C programming for these DSP processors. Finally we show that the paired-transform based algorithm of the FFT is faster than the radix-2 FFT, consequently it is useful for higher sampling rates. Working at higher data rates is a challenge in the applications of Digital Signal Processing.